Key papers

• Wan-Streamer v0.2: Higher Resolution, Same Latency (2026)
  Lianghua Huang et al.
  10.59
• Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models (2026)
  Xuan-Phi Nguyen et al.
  5.01
• Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators (2026)
  Amitash Nanda et al.
  4.39
• Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference (2026)
  Guanyu Cai et al.
  4.39
• Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices (2026)
  Jurn-Gyu Park et al.
  4.39
• x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability (2026)
  Xin Peng et al.
  4.39
• Workload-Driven Optimization for On-Device Real-Time Subtitle Translation (2026)
  Tsz-To Wong
  4.39
• Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles (2026)
  Ashiyana Abdul Majeed et al.
  4.39
• The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation (2026)
  Chenglin Yu et al.
  4.39
• How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit (2026)
  Daming Luo et al.
  4.39
• LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention (2026)
  Ziqi Yin et al.
  4.39
• Constant pH Simulation with FMM Electrostatics in GROMACS. (A) Design and Applications (2024)
  Eliane Briand et al.
  4.08
• Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels (2026)
  Joshua Brodsky et al.
  2.00
• Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations (2026)
  Mehryar Majd et al.
  2.00
• SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models (2026)
  Jinwei Kong et al.
  2.00
• Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising (2026)
  Dengyu Wu et al.
  2.00
• Stable FP4 Training via Transposition-Invariant Block Quantization (2026)
  Mehdi Rahimifar et al.
  2.00
• Bridging Compute- and Data-Optimal Pretraining (2026)
  Tian Qin et al.
  2.00
• At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference (2026)
  Bowen Wang et al.
  2.00
• Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging (2026)
  Christopher Hahne
  2.00
• Parallel Decoding Distillation for Fast Image and Video Generation (2026)
  Neta Shaul et al.
  2.00
• GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization (2026)
  Maoyang Xiang et al.
  1.89
• Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics (2026)
  Tharini Suresh et al.
  1.78
• torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch (2026)
  Mingyuan Chi et al.
  1.67
• When Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning (2025)
  Arindam Chowdhury et al.
  1.50
• What Is Next for LLMs? Next-Generation AI Computing Hardware Using Photonic Chips (2025)
  Renjie Li et al.
  1.22
• Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps (2024)
  Asif Hamid et al.
  0.72
• CLBlast: A Tuned OpenCL BLAS Library (2017)
  Cedric Nugteren
  —
• A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules (2021)
  Xinfeng Xie et al.
  —
• SciAI4Industry -- Solving PDEs for industry-scale problems with deep learning (2022)
  Philipp A. Witte et al.
  —
• Training Deep Surrogate Models with Large Scale Online Learning (2023)
  Lucas Meyer (EDF R\&D et al.
  —
• A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators (2023)
  Murali Emani et al.
  —
• Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining (2023)
  Sadasivan Shankar
  —
• FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation (2023)
  Ioannis Mavromatis and Stefano De Feo and Pietro Carnelli and Robert J. Piechocki and Aftab Khan
  —
• Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Accelerators (2024)
  Febin Sunny et al.
  —
• Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications (2024)
  Olivia Weng et al.
  —
• ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours (2024)
  Feiwen Zhu et al.
  —
• FFNet: MetaMixer-based Efficient Convolutional Mixer Design (2024)
  Seokju Yun et al.
  —
• Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications (2024)
  Youyuan Liu et al.
  —
• Unlocking Real-Time Fluorescence Lifetime Imaging: Multi-Pixel Parallelism for FPGA-Accelerated Processing (2024)
  Ismail Erbas et al.
  —